Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate Model

For practical problems with non-convex, large-scale and highly constrained characteristics, evolutionary optimisation algorithms are widely used. However, advanced data-driven methods have yet to be comprehensively applied in related fields. In this study, a surrogate model combined with the Non-dom...

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Bibliographic Details
Published in:Journal of marine science and engineering 2021-01, Vol.9 (1), p.18
Main Authors: Jiang, Jing-Wei, Yang, Yang, Ren, Tong-Wei, Wang, Fei, Huang, Wei-Xi
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Back propagation networks
data-driven evolutionary optimisation
Efficiency
Evolution
Evolutionary algorithms
Evolutionary computation
Fields
Genetic algorithms
low-frequency discrete-spectrum force
Methods
neural network
Neural networks
Noise
Noise reduction
Optimization
Propeller noise
Simulation
Sorting algorithms
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Summary:For practical problems with non-convex, large-scale and highly constrained characteristics, evolutionary optimisation algorithms are widely used. However, advanced data-driven methods have yet to be comprehensively applied in related fields. In this study, a surrogate model combined with the Non-dominated Sorting Genetic Algorithm II-Differential Evolution (NSGA-II-DE) is applied to reduce the low-frequency Discrete-Spectrum (DS) force of propeller noise. Reduction of this force has drawn a lot of attention as it is the primary signal used in the sonar-based detection and identification of ships. In the present study, a surrogate model is proposed based on a trained Back-Propagation (BP) fully connected neural network, which improves the optimisation efficiency. The neural network is designed by analysing the depth and width of the hidden layers. The results indicate that a four-layer neural network with 64, 128, 256 and 64 nodes in each layer, respectively, exhibits the highest prediction accuracy. The prediction errors for the first order of DST, second order of DST and the thrust coefficient are only 0.21%, 5.71% and 0.01%, respectively. Data-Driven Evolutionary Optimisation (DDEO) is applied to a standard high-skew propeller to reduce DST. DDEO and a Traditional Evolutionary Optimisation Method (TEOM) obtain the same optimisation results, while the time cost of DDEO is only 0.68% that of the TEOM. Thus, the proposed DDEO is applicable to complex engineering problems in various fields.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse9010018